How to graph grid scores from GridSearchCV?

Question

I am looking for a way to graph grid_scores_ from GridSearchCV in sklearn. In this example I am trying to grid search for best gamma and C parameters for an SVR algorithm. My code looks as follows:

    C_range = 10.0 ** np.arange(-4, 4)     gamma_range = 10.0 ** np.arange(-4, 4)     param_grid = dict(gamma=gamma_range.tolist(), C=C_range.tolist())     grid = GridSearchCV(SVR(kernel='rbf', gamma=0.1),param_grid, cv=5)     grid.fit(X_train,y_train)     print(grid.grid_scores_) 

After I run the code and print the grid scores I get the following outcome:

[mean: -3.28593, std: 1.69134, params: {'gamma': 0.0001, 'C': 0.0001}, mean: -3.29370, std: 1.69346, params: {'gamma': 0.001, 'C': 0.0001}, mean: -3.28933, std: 1.69104, params: {'gamma': 0.01, 'C': 0.0001}, mean: -3.28925, std: 1.69106, params: {'gamma': 0.1, 'C': 0.0001}, mean: -3.28925, std: 1.69106, params: {'gamma': 1.0, 'C': 0.0001}, mean: -3.28925, std: 1.69106, params: {'gamma': 10.0, 'C': 0.0001},etc]  

I would like to visualize all the scores (mean values) depending on gamma and C parameters. The graph I am trying to obtain should look as follows:

Where x-axis is gamma, y-axis is mean score (root mean square error in this case), and different lines represent different C values.

Answer 1

The code shown by @sascha is correct. However, the grid_scores_ attribute will be soon deprecated. It is better to use the cv_results attribute.

It can be implemente in a similar fashion to that of @sascha method:

def plot_grid_search(cv_results, grid_param_1, grid_param_2, name_param_1, name_param_2):     # Get Test Scores Mean and std for each grid search     scores_mean = cv_results['mean_test_score']     scores_mean = np.array(scores_mean).reshape(len(grid_param_2),len(grid_param_1))      scores_sd = cv_results['std_test_score']     scores_sd = np.array(scores_sd).reshape(len(grid_param_2),len(grid_param_1))      # Plot Grid search scores     _, ax = plt.subplots(1,1)      # Param1 is the X-axis, Param 2 is represented as a different curve (color line)     for idx, val in enumerate(grid_param_2):         ax.plot(grid_param_1, scores_mean[idx,:], '-o', label= name_param_2 + ': ' + str(val))      ax.set_title("Grid Search Scores", fontsize=20, fontweight='bold')     ax.set_xlabel(name_param_1, fontsize=16)     ax.set_ylabel('CV Average Score', fontsize=16)     ax.legend(loc="best", fontsize=15)     ax.grid('on')  # Calling Method  plot_grid_search(pipe_grid.cv_results_, n_estimators, max_features, 'N Estimators', 'Max Features') 

The above results in the following plot:

Answer 2

from sklearn.svm import SVC from sklearn.grid_search import GridSearchCV from sklearn import datasets import matplotlib.pyplot as plt import seaborn as sns import numpy as np  digits = datasets.load_digits() X = digits.data y = digits.target  clf_ = SVC(kernel='rbf') Cs = [1, 10, 100, 1000] Gammas = [1e-3, 1e-4] clf = GridSearchCV(clf_,             dict(C=Cs,                  gamma=Gammas),                  cv=2,                  pre_dispatch='1*n_jobs',                  n_jobs=1)  clf.fit(X, y)  scores = [x[1] for x in clf.grid_scores_] scores = np.array(scores).reshape(len(Cs), len(Gammas))  for ind, i in enumerate(Cs):     plt.plot(Gammas, scores[ind], label='C: ' + str(i)) plt.legend() plt.xlabel('Gamma') plt.ylabel('Mean score') plt.show() 

• Code is based on this.
• Only puzzling part: will sklearn always respect the order of C & Gamma -> official example uses this "ordering"

Output: